Abstract Autism spectrum disorder (ASD) is a heterogeneous disease with an unknown etiology. The global increase in ASD incidence suggests that genetics alone is unlikely to be the major driver of ASD, but that the increased prevalence is likely due to altered exposures to environmental factors. In fact, we know that numerous environmental exposures (nutrients, chemicals, stress, etc.) impact child health, typically exerting their toxicity through either metabolites or perturbations in endogenous pathways, making metabolomics analysis a key emerging technology to elucidate the relationships between these exposures and ASD. But how do we directly measure these early life exposures? Central to our study is the use of novel tooth matrix biomarkers, which takes advantage of the incremental developmental biology of teeth (similar to tree growth rings). The techniques that we have developed allow us to temporally distinguish exposure between the 2nd trimester, 3rd trimesters, and postnatal periods, enabling identification of the sensitive life stages in fetal and neonatal development most strongly associated with ASD risk. For the present application, we will perform the first targeted organic analysis of ASD teeth to delineate associations between toxicant mixtures from various exposure sources (polybrominated diphenyl ethers (PBDEs), phthalates, and organochlorine pesticides) and autism. This will be supported by the first large-scale untargeted metabolomics analysis of ASD teeth to delineate unique metabolic alterations in corresponding autism and non-autism children, and generate new hypothesis on early life etiology of ASD. As both analyses will be executed in the same tooth extract, we will also perform a multifactorial analysis, exploring the relationships between targeted toxicant exposures, metabolomics profiles, and ASD. We will undertake this work in the Childhood Autism Risks from Genetics and the Environment (CHARGE) cohort, which has a wealth of harmonized phenotypic, demographic, medical, genetic, and environmental data for high efficiency analysis. We will use novel statistical methodology, weighted quantile sum regression (WQS), that addresses effects of high-dimensional mixtures and increases power when compared to traditional methods to discover biomarkers and biological pathways associated with ASD (n=318) or typical development (n=190) (neither ASD nor other developmental delays (n=105)). Our method is a non-invasive advancement in technology to obtain direct and repeated fetal measures of biomarkers associated with early life etiology of ASD.